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PubMed:28764868 JSONTXT 8 Projects

Annnotations TAB TSV DIC JSON TextAE Lectin_function IAV-Glycan

Id Subject Object Predicate Lexical cue
T1 0-118 Sentence denotes Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection.
T1 0-118 Sentence denotes Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection.
T2 119-277 Sentence denotes The current treatment paradigm in Clostridium difficile infection is the administration of antibiotics contributing to the high rates of recurrent infections.
T2 119-277 Sentence denotes The current treatment paradigm in Clostridium difficile infection is the administration of antibiotics contributing to the high rates of recurrent infections.
T3 278-464 Sentence denotes Recent alternative strategies, such as fecal microbiome transplantation and anti-toxin antibodies, have shown similar efficacy in the treatment of C. difficile associated disease (CDAD).
T3 278-464 Sentence denotes Recent alternative strategies, such as fecal microbiome transplantation and anti-toxin antibodies, have shown similar efficacy in the treatment of C. difficile associated disease (CDAD).
T4 465-584 Sentence denotes However, barriers exist for either treatment or other novel treatments to displace antibiotics as the standard of care.
T4 465-584 Sentence denotes However, barriers exist for either treatment or other novel treatments to displace antibiotics as the standard of care.
T5 585-739 Sentence denotes To aid in the comparison of these and future treatments in CDAD, we developed an in silico pipeline to predict clinical efficacy with nonclinical results.
T5 585-739 Sentence denotes To aid in the comparison of these and future treatments in CDAD, we developed an in silico pipeline to predict clinical efficacy with nonclinical results.
T6 740-1146 Sentence denotes The pipeline combines an ordinary differential equation (ODE)-based model, describing the immunological and microbial interactions in the gastrointestinal (GI) mucosa, with machine learning algorithms to translate simulated output quantities (i.e. time of clearance, quantity of commensal bacteria, T cell ratios) into clinical predictions based on prior preclinical, translational and clinical trial data.
T6 740-1146 Sentence denotes The pipeline combines an ordinary differential equation (ODE)-based model, describing the immunological and microbial interactions in the gastrointestinal (GI) mucosa, with machine learning algorithms to translate simulated output quantities (i.e. time of clearance, quantity of commensal bacteria, T cell ratios) into clinical predictions based on prior preclinical, translational and clinical trial data.
T7 1147-1437 Sentence denotes As a use case, we compare the efficacy of lanthionine synthetase C-like 2 (LANCL2), a novel immunoregulatory target with promising efficacy in inflammatory bowel disease (IBD), activation with antibiotics, fecal microbiome transplantation and anti-toxin antibodies in the treatment of CDAD.
T7 1147-1437 Sentence denotes As a use case, we compare the efficacy of lanthionine synthetase C-like 2 (LANCL2), a novel immunoregulatory target with promising efficacy in inflammatory bowel disease (IBD), activation with antibiotics, fecal microbiome transplantation and anti-toxin antibodies in the treatment of CDAD.
T8 1438-1664 Sentence denotes We further validate the potential of LANCL2 pathway activation, in a mouse model of C. difficile infection in which it displays an ability to decrease weight loss and inflammatory cell types while protecting against mortality.
T8 1438-1664 Sentence denotes We further validate the potential of LANCL2 pathway activation, in a mouse model of C. difficile infection in which it displays an ability to decrease weight loss and inflammatory cell types while protecting against mortality.
T9 1665-1774 Sentence denotes The computational pipeline can serve as an important resource in the development of new treatment modalities.
T9 1665-1774 Sentence denotes The computational pipeline can serve as an important resource in the development of new treatment modalities.